"""simple docstring"""



import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def  SCREAMING_SNAKE_CASE							(  snake_case):
     __snake_case				=				SwinConfig()
     __snake_case				=				swin_name.split('''_''')

     __snake_case				=				name_split[1]
     __snake_case				=				int(name_split[4])
     __snake_case				=				int(name_split[3][-1])

     if model_size == "tiny":
          __snake_case				=				96
          __snake_case				=				(2, 2, 6, 2)
          __snake_case				=				(3, 6, 12, 24)
     elif model_size == "small":
          __snake_case				=				96
          __snake_case				=				(2, 2, 18, 2)
          __snake_case				=				(3, 6, 12, 24)
     elif model_size == "base":
          __snake_case				=				1_28
          __snake_case				=				(2, 2, 18, 2)
          __snake_case				=				(4, 8, 16, 32)
     else:
          __snake_case				=				1_92
          __snake_case				=				(2, 2, 18, 2)
          __snake_case				=				(6, 12, 24, 48)

     if "in22k" in swin_name:
          __snake_case				=				2_18_41
     else:
          __snake_case				=				10_00
          __snake_case				=				'''huggingface/label-files'''
          __snake_case				=				'''imagenet-1k-id2label.json'''
          __snake_case				=				json.load(open(hf_hub_download(_A,   _A,   repo_type='''dataset'''),   '''r'''))
          __snake_case				=				{int(_A): v for k, v in idalabel.items()}
          __snake_case				=				idalabel
          __snake_case				=				{v: k for k, v in idalabel.items()}

     __snake_case				=				img_size
     __snake_case				=				num_classes
     __snake_case				=				embed_dim
     __snake_case				=				depths
     __snake_case				=				num_heads
     __snake_case				=				window_size

     return config





def  SCREAMING_SNAKE_CASE							(  snake_case):

     if "patch_embed.proj" in name:
          __snake_case				=				name.replace('''patch_embed.proj''',   '''embeddings.patch_embeddings.projection''')
     if "patch_embed.norm" in name:
          __snake_case				=				name.replace('''patch_embed.norm''',   '''embeddings.norm''')
     if "layers" in name:
          __snake_case				=				'''encoder.''' + name
     if "attn.proj" in name:
          __snake_case				=				name.replace('''attn.proj''',   '''attention.output.dense''')
     if "attn" in name:
          __snake_case				=				name.replace('''attn''',   '''attention.self''')
     if "norm1" in name:
          __snake_case				=				name.replace('''norm1''',   '''layernorm_before''')
     if "norm2" in name:
          __snake_case				=				name.replace('''norm2''',   '''layernorm_after''')
     if "mlp.fc1" in name:
          __snake_case				=				name.replace('''mlp.fc1''',   '''intermediate.dense''')
     if "mlp.fc2" in name:
          __snake_case				=				name.replace('''mlp.fc2''',   '''output.dense''')

     if name == "norm.weight":
          __snake_case				=				'''layernorm.weight'''
     if name == "norm.bias":
          __snake_case				=				'''layernorm.bias'''

     if "head" in name:
          __snake_case				=				name.replace('''head''',   '''classifier''')
     else:
          __snake_case				=				'''swin.''' + name

     return name





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):

     for key in orig_state_dict.copy().keys():
          __snake_case				=				orig_state_dict.pop(_A)

          if "mask" in key:
               continue
          elif "qkv" in key:
               __snake_case				=				key.split('''.''')
               __snake_case				=				int(key_split[1])
               __snake_case				=				int(key_split[3])
               __snake_case				=				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    __snake_case				=				val[:dim, :]
                    __snake_case				=				val[
                        dim : dim * 2, :
                    ]
                    __snake_case				=				val[-dim:, :]
               else:
                    __snake_case				=				val[
                        :dim
                    ]
                    __snake_case				=				val[
                        dim : dim * 2
                    ]
                    __snake_case				=				val[
                        -dim:
                    ]
          else:
               __snake_case				=				val

     return orig_state_dict





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):
     __snake_case				=				timm.create_model(_A,   pretrained=_A)
     timm_model.eval()

     __snake_case				=				get_swin_config(_A)
     __snake_case				=				SwinForImageClassification(_A)
     model.eval()

     __snake_case				=				convert_state_dict(timm_model.state_dict(),   _A)
     model.load_state_dict(_A)

     __snake_case				=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

     __snake_case				=				AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''',   '''-''')))
     __snake_case				=				Image.open(requests.get(_A,   stream=_A).raw)
     __snake_case				=				image_processor(images=_A,   return_tensors='''pt''')

     __snake_case				=				timm_model(inputs['''pixel_values'''])
     __snake_case				=				model(**_A).logits

     assert torch.allclose(_A,   _A,   atol=1E-3)

     print(f"Saving model {swin_name} to {pytorch_dump_folder_path}")
     model.save_pretrained(_A)

     print(f"Saving image processor to {pytorch_dump_folder_path}")
     image_processor.save_pretrained(_A)


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]								=				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--swin_name",
           default="swin_tiny_patch4_window7_224",
           type=str,
           help="Name of the Swin timm model you\'d like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       __lowercase		:		Optional[int]								=				parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)

"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()